arXiv subjects

Georgios Fontaras

Publications and source records attributed to Georgios Fontaras.

3 recordsLinked to original sources

arXiv2026

ARA: Agentic Reproducibility Assessment For Scalable Support Of Scientific Peer-Review

Scientific peer review increasingly struggles to assess reproducibility at the scale and complexity of modern research output. Evaluating reproducibility requires reconstructing experimental dependencies, methodological choices, data flows, and result-generating procedures, which often exceeds what human reviewers can provide. Agentic Reproducibility Assessment (ARA) formalizes reproducibility assessment as a structured reasoning task over scientific documents. Given a paper, ARA extracts a directed workflow graph linking sources, methods, experiments, and outputs, then evaluates its reconstructability using structural and content-based scores for reproducibility assessments. Experiments on 213 ReScience C articles - the largest cross-domain benchmark of human-validated computational reproducibility studies considered to date - demonstrate ARA's generalizability and consistent workflow reconstruction and assessment across LLMs, model temperatures, and scientific domains. ARA achieves ~61% accuracy on three benchmarks, and the highest accuracy reported on ReproBench (60.71% vs. 36.84%) and GoldStandardDB (61.68% vs. 43.56%), highlighting its potential to complement human review at scale and enabling next-generation peer review. Code and Data available: https://github.com/AndresLaverdeMarin/agentic_reproducibility_assessment.

cs.DL↗

arXiv2022

Characterizing driver heterogeneity within stochastic traffic simulation

Drivers' heterogeneity and the broad range of vehicle characteristics on public roads are primarily responsible for the stochasticity observed in road traffic dynamics. Understanding the behavioural differences in drivers (human or automated systems) and reproducing observed behaviours in microsimulation attracts significant attention lately. Calibration of car-following model parameters is the prevalent way to simulate different driving behaviors through randomly injected variation around average parameter values. An issue is that, as shown in the literature, most car-following model do not realistically reproduce free-flow acceleration, that is in turn highly correlated with heterogeneity in driving styles. Furthermore, often, model parameters lose their physical interpretation upon calibration. The present study proposes a novel framework to analyse observed vehicle trajectories from various drivers, identify individual driver fingerprints based on their acceleration behavior, cluster drivers in categorically-meaningful driving styles (e.g. mild, normal and aggressive) and reproduce observed individual driver acceleration behaviors in microsimulation.

physics.soc-ph↗

arXiv2019

The impact of automation and connectivity on traffic flow and CO2 emissions. A detailed microsimulation study

Many of the anticipated advantages of connected and automated vehicles or automated vehicles without connectivity (CAVs and AVs respectively) on congestion and energy consumption are questionable. Some studies provide quantitative answers to the above questions through microsimulation but they systematically ignore the realistic simulation of vehicle dynamics, driver behaviour or instantaneous emissions estimates, mostly due to the overall increased complexity of the transport systems and the need for low computational demand on large-scale simulations. However, recent studies question the capability of common car-following models to produce realistic vehicle dynamics or driving behaviour, which directly impacts emissions estimations as well. This work presents a microsimulation study that contributes on the topic, using a scenario-based approach to give insights regarding the impact of CAVs and AVs on the evolution of emissions over a highway network. The motivation here is to answer whether the different driving behaviours produce significant differences in emissions during rush hours, and how significant is the impact of detailed vehicle dynamics simulation and instantaneous emissions in the outcome.

physics.soc-ph↗